Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations907
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory353.6 KiB
Average record size in memory399.2 B

Variable types

Text4
Categorical1
Numeric7

Alerts

Duration_Years is highly overall correlated with LevelHigh correlation
Exchange_Rate is highly overall correlated with Insurance_USDHigh correlation
Insurance_USD is highly overall correlated with Exchange_Rate and 2 other fieldsHigh correlation
Level is highly overall correlated with Duration_YearsHigh correlation
Living_Cost_Index is highly overall correlated with Insurance_USD and 1 other fieldsHigh correlation
Rent_USD is highly overall correlated with Insurance_USD and 2 other fieldsHigh correlation
Tuition_USD is highly overall correlated with Rent_USD and 1 other fieldsHigh correlation
Visa_Fee_USD is highly overall correlated with Tuition_USDHigh correlation
Tuition_USD has 103 (11.4%) zeros Zeros

Reproduction

Analysis started2025-05-24 15:45:55.627966
Analysis finished2025-05-24 15:46:07.557385
Duration11.93 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct71
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Memory size56.4 KiB
2025-05-24T11:46:08.085936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length18
Median length12
Mean length6.5104741
Min length2

Characters and Unicode

Total characters5905
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.8%

Sample

1st rowUSA
2nd rowUK
3rd rowCanada
4th rowAustralia
5th rowGermany
ValueCountFrequency (%)
uk 93
 
9.7%
australia 86
 
8.9%
usa 78
 
8.1%
canada 76
 
7.9%
germany 33
 
3.4%
france 27
 
2.8%
south 24
 
2.5%
korea 23
 
2.4%
netherlands 21
 
2.2%
switzerland 20
 
2.1%
Other values (67) 481
50.0%
2025-05-24T11:46:08.763968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 996
16.9%
n 445
 
7.5%
r 419
 
7.1%
e 382
 
6.5%
i 360
 
6.1%
l 275
 
4.7%
u 236
 
4.0%
d 220
 
3.7%
t 216
 
3.7%
A 203
 
3.4%
Other values (36) 2153
36.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5905
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 996
16.9%
n 445
 
7.5%
r 419
 
7.1%
e 382
 
6.5%
i 360
 
6.1%
l 275
 
4.7%
u 236
 
4.0%
d 220
 
3.7%
t 216
 
3.7%
A 203
 
3.4%
Other values (36) 2153
36.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5905
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 996
16.9%
n 445
 
7.5%
r 419
 
7.1%
e 382
 
6.5%
i 360
 
6.1%
l 275
 
4.7%
u 236
 
4.0%
d 220
 
3.7%
t 216
 
3.7%
A 203
 
3.4%
Other values (36) 2153
36.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5905
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 996
16.9%
n 445
 
7.5%
r 419
 
7.1%
e 382
 
6.5%
i 360
 
6.1%
l 275
 
4.7%
u 236
 
4.0%
d 220
 
3.7%
t 216
 
3.7%
A 203
 
3.4%
Other values (36) 2153
36.5%

City
Text

Distinct556
Distinct (%)61.3%
Missing0
Missing (%)0.0%
Memory size57.6 KiB
2025-05-24T11:46:09.286743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length17
Median length15
Mean length7.567806
Min length2

Characters and Unicode

Total characters6864
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique390 ?
Unique (%)43.0%

Sample

1st rowCambridge
2nd rowLondon
3rd rowToronto
4th rowMelbourne
5th rowMunich
ValueCountFrequency (%)
singapore 18
 
1.8%
melbourne 11
 
1.1%
sydney 11
 
1.1%
london 11
 
1.1%
city 10
 
1.0%
san 9
 
0.9%
canberra 8
 
0.8%
brisbane 7
 
0.7%
newcastle 7
 
0.7%
coast 7
 
0.7%
Other values (585) 900
90.1%
2025-05-24T11:46:09.987037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 749
 
10.9%
e 590
 
8.6%
n 530
 
7.7%
o 507
 
7.4%
r 457
 
6.7%
i 407
 
5.9%
l 313
 
4.6%
t 299
 
4.4%
s 273
 
4.0%
u 227
 
3.3%
Other values (52) 2512
36.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6864
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 749
 
10.9%
e 590
 
8.6%
n 530
 
7.7%
o 507
 
7.4%
r 457
 
6.7%
i 407
 
5.9%
l 313
 
4.6%
t 299
 
4.4%
s 273
 
4.0%
u 227
 
3.3%
Other values (52) 2512
36.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6864
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 749
 
10.9%
e 590
 
8.6%
n 530
 
7.7%
o 507
 
7.4%
r 457
 
6.7%
i 407
 
5.9%
l 313
 
4.6%
t 299
 
4.4%
s 273
 
4.0%
u 227
 
3.3%
Other values (52) 2512
36.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6864
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 749
 
10.9%
e 590
 
8.6%
n 530
 
7.7%
o 507
 
7.4%
r 457
 
6.7%
i 407
 
5.9%
l 313
 
4.6%
t 299
 
4.4%
s 273
 
4.0%
u 227
 
3.3%
Other values (52) 2512
36.6%
Distinct622
Distinct (%)68.6%
Missing0
Missing (%)0.0%
Memory size69.1 KiB
2025-05-24T11:46:10.562585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length50
Median length34
Mean length20.040794
Min length3

Characters and Unicode

Total characters18177
Distinct characters67
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique454 ?
Unique (%)50.1%

Sample

1st rowHarvard University
2nd rowImperial College London
3rd rowUniversity of Toronto
4th rowUniversity of Melbourne
5th rowTechnical University of Munich
ValueCountFrequency (%)
university 688
28.5%
of 389
 
16.1%
universidad 34
 
1.4%
national 25
 
1.0%
de 23
 
1.0%
technology 17
 
0.7%
state 15
 
0.6%
tu 15
 
0.6%
college 12
 
0.5%
southern 10
 
0.4%
Other values (699) 1189
49.2%
2025-05-24T11:46:11.333879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 1998
 
11.0%
1510
 
8.3%
e 1501
 
8.3%
n 1413
 
7.8%
r 1184
 
6.5%
t 1104
 
6.1%
s 1057
 
5.8%
o 964
 
5.3%
a 885
 
4.9%
U 838
 
4.6%
Other values (57) 5723
31.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18177
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 1998
 
11.0%
1510
 
8.3%
e 1501
 
8.3%
n 1413
 
7.8%
r 1184
 
6.5%
t 1104
 
6.1%
s 1057
 
5.8%
o 964
 
5.3%
a 885
 
4.9%
U 838
 
4.6%
Other values (57) 5723
31.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18177
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 1998
 
11.0%
1510
 
8.3%
e 1501
 
8.3%
n 1413
 
7.8%
r 1184
 
6.5%
t 1104
 
6.1%
s 1057
 
5.8%
o 964
 
5.3%
a 885
 
4.9%
U 838
 
4.6%
Other values (57) 5723
31.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18177
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 1998
 
11.0%
1510
 
8.3%
e 1501
 
8.3%
n 1413
 
7.8%
r 1184
 
6.5%
t 1104
 
6.1%
s 1057
 
5.8%
o 964
 
5.3%
a 885
 
4.9%
U 838
 
4.6%
Other values (57) 5723
31.5%
Distinct92
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Memory size65.4 KiB
2025-05-24T11:46:11.671684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length25
Median length23
Mean length16.637266
Min length6

Characters and Unicode

Total characters15090
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)5.8%

Sample

1st rowComputer Science
2nd rowData Science
3rd rowBusiness Analytics
4th rowEngineering
5th rowMechanical Engineering
ValueCountFrequency (%)
science 411
23.6%
computer 388
22.3%
engineering 184
10.6%
data 153
 
8.8%
software 83
 
4.8%
information 68
 
3.9%
analytics 55
 
3.2%
artificial 53
 
3.0%
intelligence 53
 
3.0%
systems 53
 
3.0%
Other values (60) 240
13.8%
2025-05-24T11:46:12.200271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 2094
13.9%
n 1435
 
9.5%
i 1303
 
8.6%
c 1137
 
7.5%
t 1058
 
7.0%
r 843
 
5.6%
834
 
5.5%
o 804
 
5.3%
a 679
 
4.5%
m 583
 
3.9%
Other values (30) 4320
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15090
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2094
13.9%
n 1435
 
9.5%
i 1303
 
8.6%
c 1137
 
7.5%
t 1058
 
7.0%
r 843
 
5.6%
834
 
5.5%
o 804
 
5.3%
a 679
 
4.5%
m 583
 
3.9%
Other values (30) 4320
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15090
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2094
13.9%
n 1435
 
9.5%
i 1303
 
8.6%
c 1137
 
7.5%
t 1058
 
7.0%
r 843
 
5.6%
834
 
5.5%
o 804
 
5.3%
a 679
 
4.5%
m 583
 
3.9%
Other values (30) 4320
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15090
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2094
13.9%
n 1435
 
9.5%
i 1303
 
8.6%
c 1137
 
7.5%
t 1058
 
7.0%
r 843
 
5.6%
834
 
5.5%
o 804
 
5.3%
a 679
 
4.5%
m 583
 
3.9%
Other values (30) 4320
28.6%

Level
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size56.0 KiB
Master
451 
Bachelor
297 
PhD
159 

Length

Max length8
Median length6
Mean length6.1289967
Min length3

Characters and Unicode

Total characters5559
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMaster
2nd rowMaster
3rd rowMaster
4th rowMaster
5th rowMaster

Common Values

ValueCountFrequency (%)
Master 451
49.7%
Bachelor 297
32.7%
PhD 159
 
17.5%

Length

2025-05-24T11:46:12.336680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-24T11:46:12.446053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
master 451
49.7%
bachelor 297
32.7%
phd 159
 
17.5%

Most occurring characters

ValueCountFrequency (%)
a 748
13.5%
e 748
13.5%
r 748
13.5%
h 456
8.2%
M 451
8.1%
s 451
8.1%
t 451
8.1%
B 297
 
5.3%
c 297
 
5.3%
l 297
 
5.3%
Other values (3) 615
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5559
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 748
13.5%
e 748
13.5%
r 748
13.5%
h 456
8.2%
M 451
8.1%
s 451
8.1%
t 451
8.1%
B 297
 
5.3%
c 297
 
5.3%
l 297
 
5.3%
Other values (3) 615
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5559
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 748
13.5%
e 748
13.5%
r 748
13.5%
h 456
8.2%
M 451
8.1%
s 451
8.1%
t 451
8.1%
B 297
 
5.3%
c 297
 
5.3%
l 297
 
5.3%
Other values (3) 615
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5559
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 748
13.5%
e 748
13.5%
r 748
13.5%
h 456
8.2%
M 451
8.1%
s 451
8.1%
t 451
8.1%
B 297
 
5.3%
c 297
 
5.3%
l 297
 
5.3%
Other values (3) 615
11.1%

Duration_Years
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8368247
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2025-05-24T11:46:12.562885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile4
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.94544922
Coefficient of variation (CV)0.33327728
Kurtosis-1.2433907
Mean2.8368247
Median Absolute Deviation (MAD)1
Skewness0.33722348
Sum2573
Variance0.89387423
MonotonicityNot monotonic
2025-05-24T11:46:12.687644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 423
46.6%
4 276
30.4%
3 171
18.9%
5 17
 
1.9%
1 16
 
1.8%
2.5 3
 
0.3%
1.5 1
 
0.1%
ValueCountFrequency (%)
1 16
 
1.8%
1.5 1
 
0.1%
2 423
46.6%
2.5 3
 
0.3%
3 171
18.9%
4 276
30.4%
5 17
 
1.9%
ValueCountFrequency (%)
5 17
 
1.9%
4 276
30.4%
3 171
18.9%
2.5 3
 
0.3%
2 423
46.6%
1.5 1
 
0.1%
1 16
 
1.8%

Tuition_USD
Real number (ℝ)

High correlation  Zeros 

Distinct274
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16705.017
Minimum0
Maximum62000
Zeros103
Zeros (%)11.4%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2025-05-24T11:46:12.989735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12850
median7500
Q331100
95-th percentile48000
Maximum62000
Range62000
Interquartile range (IQR)28250

Descriptive statistics

Standard deviation16582.385
Coefficient of variation (CV)0.99265902
Kurtosis-0.79538039
Mean16705.017
Median Absolute Deviation (MAD)7500
Skewness0.71268849
Sum15151450
Variance2.749755 × 108
MonotonicityNot monotonic
2025-05-24T11:46:13.213049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 103
 
11.4%
3500 21
 
2.3%
1500 20
 
2.2%
4200 15
 
1.7%
3800 14
 
1.5%
35000 13
 
1.4%
4500 12
 
1.3%
4000 10
 
1.1%
3200 10
 
1.1%
31200 10
 
1.1%
Other values (264) 679
74.9%
ValueCountFrequency (%)
0 103
11.4%
400 7
 
0.8%
450 3
 
0.3%
500 9
 
1.0%
800 2
 
0.2%
900 2
 
0.2%
1000 3
 
0.3%
1100 2
 
0.2%
1200 4
 
0.4%
1300 3
 
0.3%
ValueCountFrequency (%)
62000 2
 
0.2%
58000 4
0.4%
57000 1
 
0.1%
56000 3
0.3%
55800 1
 
0.1%
55400 1
 
0.1%
55000 1
 
0.1%
54500 1
 
0.1%
54200 1
 
0.1%
54000 5
0.6%

Living_Cost_Index
Real number (ℝ)

High correlation 

Distinct225
Distinct (%)24.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.437486
Minimum27.8
Maximum122.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2025-05-24T11:46:13.408982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum27.8
5-th percentile39.8
Q156.3
median67.5
Q372.2
95-th percentile83.8
Maximum122.4
Range94.6
Interquartile range (IQR)15.9

Descriptive statistics

Standard deviation14.056333
Coefficient of variation (CV)0.21813906
Kurtosis0.73565883
Mean64.437486
Median Absolute Deviation (MAD)6
Skewness-0.1066651
Sum58444.8
Variance197.58049
MonotonicityNot monotonic
2025-05-24T11:46:13.633289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71.2 28
 
3.1%
68.9 28
 
3.1%
69.8 23
 
2.5%
65.4 22
 
2.4%
68.5 20
 
2.2%
67.8 18
 
2.0%
65.8 18
 
2.0%
64.5 16
 
1.8%
83.2 14
 
1.5%
72.8 13
 
1.4%
Other values (215) 707
77.9%
ValueCountFrequency (%)
27.8 1
0.1%
28.5 1
0.1%
29.8 2
0.2%
30.5 2
0.2%
31.2 1
0.1%
31.8 2
0.2%
32.5 1
0.1%
33.2 2
0.2%
34.5 1
0.1%
34.8 1
0.1%
ValueCountFrequency (%)
122.4 1
 
0.1%
119.8 1
 
0.1%
116.5 1
 
0.1%
114.3 1
 
0.1%
112.1 1
 
0.1%
110.4 1
 
0.1%
108.9 1
 
0.1%
107.8 1
 
0.1%
100 3
0.3%
95.2 3
0.3%

Rent_USD
Real number (ℝ)

High correlation 

Distinct67
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean969.20617
Minimum150
Maximum2500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2025-05-24T11:46:13.827880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile260
Q1545
median900
Q31300
95-th percentile1900
Maximum2500
Range2350
Interquartile range (IQR)755

Descriptive statistics

Standard deviation517.15475
Coefficient of variation (CV)0.5335859
Kurtosis-0.32234527
Mean969.20617
Median Absolute Deviation (MAD)400
Skewness0.53315801
Sum879070
Variance267449.04
MonotonicityNot monotonic
2025-05-24T11:46:14.021645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1100 54
 
6.0%
900 51
 
5.6%
1200 47
 
5.2%
1000 46
 
5.1%
800 41
 
4.5%
950 40
 
4.4%
1500 39
 
4.3%
1400 36
 
4.0%
1300 36
 
4.0%
400 31
 
3.4%
Other values (57) 486
53.6%
ValueCountFrequency (%)
150 1
 
0.1%
160 4
 
0.4%
170 3
 
0.3%
180 6
0.7%
200 7
0.8%
220 4
 
0.4%
230 2
 
0.2%
240 5
 
0.6%
250 13
1.4%
260 6
0.7%
ValueCountFrequency (%)
2500 3
 
0.3%
2400 3
 
0.3%
2300 5
 
0.6%
2200 8
 
0.9%
2100 8
 
0.9%
2000 15
1.7%
1900 18
2.0%
1850 1
 
0.1%
1800 23
2.5%
1700 21
2.3%

Visa_Fee_USD
Real number (ℝ)

High correlation 

Distinct30
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean211.39691
Minimum40
Maximum490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2025-05-24T11:46:14.208092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile80
Q1100
median160
Q3240
95-th percentile490
Maximum490
Range450
Interquartile range (IQR)140

Descriptive statistics

Standard deviation143.43574
Coefficient of variation (CV)0.67851388
Kurtosis-0.55259212
Mean211.39691
Median Absolute Deviation (MAD)70
Skewness1.0000705
Sum191737
Variance20573.811
MonotonicityNot monotonic
2025-05-24T11:46:14.364652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
160 103
11.4%
450 96
 
10.6%
235 76
 
8.4%
90 68
 
7.5%
120 65
 
7.2%
100 57
 
6.3%
80 53
 
5.8%
490 50
 
5.5%
485 43
 
4.7%
140 42
 
4.6%
Other values (20) 254
28.0%
ValueCountFrequency (%)
40 8
 
0.9%
60 6
 
0.7%
70 6
 
0.7%
75 21
 
2.3%
80 53
5.8%
88 12
 
1.3%
90 68
7.5%
99 27
 
3.0%
100 57
6.3%
110 36
4.0%
ValueCountFrequency (%)
490 50
5.5%
485 43
4.7%
450 96
10.6%
350 10
 
1.1%
330 8
 
0.9%
275 10
 
1.1%
270 8
 
0.9%
250 1
 
0.1%
245 1
 
0.1%
235 76
8.4%

Insurance_USD
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean700.07718
Minimum200
Maximum1500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2025-05-24T11:46:14.539059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum200
5-th percentile259
Q1450
median650
Q3800
95-th percentile1500
Maximum1500
Range1300
Interquartile range (IQR)350

Descriptive statistics

Standard deviation320.37487
Coefficient of variation (CV)0.45762794
Kurtosis1.0388359
Mean700.07718
Median Absolute Deviation (MAD)150
Skewness1.01755
Sum634970
Variance102640.06
MonotonicityNot monotonic
2025-05-24T11:46:14.687178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
800 167
18.4%
650 110
12.1%
600 85
9.4%
1500 78
8.6%
400 78
8.6%
750 61
 
6.7%
300 45
 
5.0%
900 42
 
4.6%
700 31
 
3.4%
350 28
 
3.1%
Other values (10) 182
20.1%
ValueCountFrequency (%)
200 22
 
2.4%
250 24
 
2.6%
280 10
 
1.1%
300 45
5.0%
320 5
 
0.6%
350 28
 
3.1%
400 78
8.6%
450 18
 
2.0%
500 24
 
2.6%
550 14
 
1.5%
ValueCountFrequency (%)
1500 78
8.6%
1200 20
 
2.2%
900 42
 
4.6%
850 24
 
2.6%
800 167
18.4%
750 61
 
6.7%
720 21
 
2.3%
700 31
 
3.4%
650 110
12.1%
600 85
9.4%

Exchange_Rate
Real number (ℝ)

High correlation 

Distinct65
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean623.00069
Minimum0.15
Maximum42150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2025-05-24T11:46:14.861309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.15
5-th percentile0.79
Q10.92
median1.35
Q37.15
95-th percentile1300
Maximum42150
Range42149.85
Interquartile range (IQR)6.23

Descriptive statistics

Standard deviation3801.7461
Coefficient of variation (CV)6.1023144
Kurtosis81.570863
Mean623.00069
Median Absolute Deviation (MAD)0.43
Skewness8.4824754
Sum565061.63
Variance14453274
MonotonicityNot monotonic
2025-05-24T11:46:15.058871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.92 198
21.8%
1 93
 
10.3%
0.79 93
 
10.3%
1.52 86
 
9.5%
1.35 82
 
9.0%
10.45 21
 
2.3%
0.89 20
 
2.2%
1300 20
 
2.2%
4.65 16
 
1.8%
3.75 16
 
1.8%
Other values (55) 262
28.9%
ValueCountFrequency (%)
0.15 3
 
0.3%
0.31 5
 
0.6%
0.38 5
 
0.6%
0.79 93
10.3%
0.89 20
 
2.2%
0.92 198
21.8%
1 93
10.3%
1.34 12
 
1.3%
1.35 82
9.0%
1.52 86
9.5%
ValueCountFrequency (%)
42150 5
 
0.6%
24450 1
 
0.1%
15640 1
 
0.1%
15600 7
 
0.8%
15000 5
 
0.6%
12300 5
 
0.6%
3950 6
 
0.7%
1320.5 3
 
0.3%
1300 20
2.2%
860.2 1
 
0.1%

Interactions

2025-05-24T11:46:05.523925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:45:56.180643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:45:57.933511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:45:59.428627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:46:00.836736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:46:02.619446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:46:04.310255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:46:05.689132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:45:56.539990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:45:58.159639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:45:59.643522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:46:01.072871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:46:02.896848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:46:04.467360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:46:05.863287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:45:56.768891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:45:58.371979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:45:59.835895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:46:01.346992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:46:03.246902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:46:04.662304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:46:06.027065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:45:56.998771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:45:58.585059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:46:00.053294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:46:01.579183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:46:03.474144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:46:04.833061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:46:06.232728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:45:57.245226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:45:58.783274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:46:00.250215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:46:01.827003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:46:03.651611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:46:04.996180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:46:06.420447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:45:57.484086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:45:59.002964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:46:00.435704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:46:02.079289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:46:03.835772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:46:05.174068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:46:06.764833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:45:57.715243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:45:59.224286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:46:00.625515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:46:02.356696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:46:04.006702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-24T11:46:05.351420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-05-24T11:46:15.190963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Duration_YearsExchange_RateInsurance_USDLevelLiving_Cost_IndexRent_USDTuition_USDVisa_Fee_USD
Duration_Years1.0000.045-0.0170.789-0.0580.0320.1280.082
Exchange_Rate0.0451.000-0.5290.000-0.453-0.462-0.202-0.234
Insurance_USD-0.017-0.5291.0000.0390.8020.7340.3710.321
Level0.7890.0000.0391.0000.0390.0870.1900.136
Living_Cost_Index-0.058-0.4530.8020.0391.0000.8320.3120.262
Rent_USD0.032-0.4620.7340.0870.8321.0000.6000.476
Tuition_USD0.128-0.2020.3710.1900.3120.6001.0000.558
Visa_Fee_USD0.082-0.2340.3210.1360.2620.4760.5581.000

Missing values

2025-05-24T11:46:07.127738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-24T11:46:07.410681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CountryCityUniversityProgramLevelDuration_YearsTuition_USDLiving_Cost_IndexRent_USDVisa_Fee_USDInsurance_USDExchange_Rate
0USACambridgeHarvard UniversityComputer ScienceMaster2.05540083.5220016015001.00
1UKLondonImperial College LondonData ScienceMaster1.04120075.818004858000.79
2CanadaTorontoUniversity of TorontoBusiness AnalyticsMaster2.03850072.516002359001.35
3AustraliaMelbourneUniversity of MelbourneEngineeringMaster2.04200071.214004506501.52
4GermanyMunichTechnical University of MunichMechanical EngineeringMaster2.050070.51100755500.92
5JapanTokyoUniversity of TokyoInformation ScienceMaster2.0890076.41300220750145.80
6NetherlandsAmsterdamUniversity of AmsterdamArtificial IntelligenceMaster1.01580073.215001807200.92
7SingaporeSingaporeNational University of SingaporeFinanceMaster1.53500081.11900908001.34
8FranceParisSorbonne UniversityInternational RelationsMaster2.0450074.61400996500.92
9SwitzerlandZurichETH ZurichPhysicsMaster2.0146091.521008812000.89
CountryCityUniversityProgramLevelDuration_YearsTuition_USDLiving_Cost_IndexRent_USDVisa_Fee_USDInsurance_USDExchange_Rate
897Saudi ArabiaMakkahUmm Al-Qura UniversityInformation TechnologyPhD4.0430065.86502008003.75
898USASan FranciscoStanford UniversityData ScienceMaster2.05500095.2240016015001.00
899UKLeedsUniversity of LeedsComputer EngineeringMaster2.03500063.29004858000.79
900SpainZaragozaUniversity of ZaragozaArtificial IntelligencePhD4.0290063.2650807500.92
901ItalyPaduaUniversity of PaduaSoftware EngineeringMaster2.0340069.59001207000.92
902FranceStrasbourgUniversity of StrasbourgData AnalyticsMaster2.0400070.21000998500.92
903MalaysiaNilaiUSIMComputer ScienceBachelor3.0680050.54001204004.65
904Saudi ArabiaAl-AhsaKing Faisal UniversityInformation SystemsMaster2.0420064.26002008003.75
905USASeattleUniversity of WashingtonSoftware DevelopmentPhD5.05000077.8200016015001.00
906UKNottinghamUniversity of NottinghamData EngineeringMaster2.03400061.28004858000.79